Policy-Robust Processing Networks: Characterization and Design

策略稳健的处理网络：表征和设计

• 批准号: 1856511
• 负责人: Itai Gurvich
• 金额: $ 48.62万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1856511&HistoricalAwards=false
• 关键词: Policy; Robust; Processing; Networks; Characterization

This award will contribute to the nation's economic competitiveness by improving the design and operation of stochastic processing networks. Stochastic processing networks, consisting of interconnected processing nodes with some flexibility to manage their own schedules, arise in a variety of settings, including many service systems and some manufacturing systems. The fact that arrival times, processing times, and routes through the network may not be predictable with certainty makes these networks particularly difficult to analyze. This award supports research into conditions for global stability of these networks where each node in the network has some autonomy and independent scheduling authority; that is, the larger network does not operate under a centralized scheduler. Stability of the network ensures that any entity entering the network will eventually be processed and leave the network. The project studies stochastic processing networks incorporating renewal arrival processes, service times with general distributions, Bernoulli routing, and multiple classes of jobs at each station. A primary interest is the global stability of these networks, i.e., stability of the network as a whole when each individual node operates under its own non-idling scheduling policy. In these networks, control is distributed, rather than being determined by a central scheduler. The major goal of this research is the development of a new framework for global stability that can support the design of operating rules that allow fairly general decentralized control. The project will lead to a better understanding of the design of robust complex networks. The techniques employed involve ideas from fluid and diffusion approximations to queueing networks, along with notions from robust optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过改进随机处理网络的设计和操作来提高国家的经济竞争力。随机加工网络由相互连接的加工节点组成，具有一定的灵活性来管理自己的调度，它出现在各种环境中，包括许多服务系统和一些制造系统。到达时间、处理时间和通过网络的路由可能无法确定地预测，这使得分析这些网络特别困难。该奖项支持对这些网络的全局稳定性条件的研究，在这些网络中，网络中的每个节点都具有一定的自治性和独立的调度权限；即，较大的网络不在中央调度器下运行。网络的稳定性确保了进入网络的任何实体最终都将被处理并离开网络。该项目研究了包含更新到达过程、服务时间服从一般分布、伯努利路径和每个站点的多类作业的随机处理网络。主要关注的是这些网络的全局稳定性，即当每个单独节点在其自己的非空闲调度策略下操作时网络作为整体的稳定性。在这些网络中，控制是分布式的，而不是由中央调度器确定的。这项研究的主要目标是开发一个新的全球稳定框架，该框架可以支持允许相当普遍的分散控制的操作规则的设计。该项目将使我们更好地理解健壮复杂网络的设计。所采用的技术涉及从流动和扩散近似到排队网络的想法，以及稳健优化的概念。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。